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Found 12 papers, 2 papers with code
Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning
A shift in data distribution can have a significant impact on performance of a text classification model.
Hierarchical Neural Collapse Detection Transformer for Class Incremental Object Detection
Recently, object detection models have witnessed notable performance improvements, particularly with transformer-based models.
Towards Rehearsal-Free Continual Relation Extraction: Capturing Within-Task Variance with Adaptive Prompting
Memory-based approaches have shown strong performance in Continual Relation Extraction (CRE).
CoT2Align: Cross-Chain of Thought Distillation via Optimal Transport Alignment for Language Models with Different Tokenizers
Large Language Models (LLMs) achieve state-of-the-art performance across various NLP tasks but face deployment challenges due to high computational costs and memory constraints.
Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
To address catastrophic forgetting in Continual Relation Extraction (CRE), many current approaches rely on memory buffers to rehearse previously learned knowledge while acquiring new tasks.
GloCOM: A Short Text Neural Topic Model via Global Clustering Context
Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemming from incomplete reconstruction targets.
Lifelong Event Detection via Optimal Transport
Continual Event Detection (CED) poses a formidable challenge due to the catastrophic forgetting phenomenon, where learning new tasks (with new coming event types) hampers performance on previous ones.
NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization
Recent advances in neural topic models have concentrated on two primary directions: the integration of the inference network (encoder) with a pre-trained language model (PLM) and the modeling of the relationship between words and topics in the generative model (decoder).
ToVo: Toxicity Taxonomy via Voting
To address these issues, we propose a dataset creation mechanism that integrates voting and chain-of-thought processes, producing a high-quality open-source dataset for toxic content detection.
Realistic Evaluation of Toxicity in Large Language Models
Large language models (LLMs) have become integral to our professional workflows and daily lives.
From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online users
In addition, we go from the hypothesis that a user's preference at a time is a combination of long-term and short-term interests.
Bag of biterms modeling for short texts
Furthermore, many applications often face with massive and dynamic short texts, causing various computational challenges to the current batch learning algorithms.
